In this paper we analysed and compared efficiency results in the banking industry using two different approaches: financial indicators and the Data Envelopment Analysis (DEA) methodology. In the indicator-based approach, we used chosen accounting ratios (Return on Assets - ROA, Return on Equity – ROE and Cost to Income Ratio - CIR) and the descriptive statistics methodology to conduct analysis. In the case of DEA, a nonparametric linear programming methodology approach, expenses as input data and income as output data are used for measuring efficiency using the CCR DEA model, BCC DEA model and window analysis DEA technique. The objective of this research is ascertain whether a correlation exists between the results of the different ways of measuring efficiency. In that sense, the main purpose of this research is to draw a more precise conclusion about the efficiency of the banking industry, as tested for the period 2008–2012 on a sample of 28 European banking systems. The main difference in the obtained results is a lag of values of average accounting ratios in comparison to the results of the DEA methodology. Such a finding suggests that the DEA methodology can be useful in detecting early signs of inadequate business strategies, which can lead to the slowdown of business activity or poorer efficiency results. This can be especially important in times of an unstable financial or macroeconomic environment, as it can assist in detecting early signs of a crisis. In general, the results of both approaches suggest that banking systems in post-transition countries have a higher cost efficiency. Such systems continue to be dominantly financed through long-term deposits and are also exposed to a specific risk. They do business in a specific competitive, financial and macroeconomic environment that significantly influences the prices of financial services (i.e. higher margins), and as a consequence, leads to potentially higher banking sector earnings.